Failure Mode and Effect Analysis for CNC machines used in GG Valves Industry

Transcription

1 Failure Mode and Effect Analysis for CNC machines used in GG Valves Industry ज ज व ल व उद य ग म स एनस मश न क ल ए ववफ त म ड पर प रभ व ववश षण Raj Kumar Salvi Thesis Masters of Technology In Mechanical Engineering (CAD/CAM) 2017 Department of Mechanical Engineering College of Technology and Engineering Maharana Pratap University of Agriculture and Technology Udaipur (Rajasthan)

2 Failure Mode and Effect Analysis for CNC machines used in GG Valves Industry ज ज व ल व उद य ग म स एनस मश न क ल ए ववफ त म ड पर प रभ व ववश षण Thesis Submitted to the Maharana Pratap University of Agriculture & Technology, Udaipur In Partial Fulfilment of the Requirement for The Degree of Master of Technology In Mechanical Engineering (CAD/CAM) By Raj Kumar Salvi 2017

3 MAHARANA PRATAP UNIVERSITY OF AGRICULTURE & TECHNOLGY, UDAIPUR COLLEGE OF TECHNOLOGY AND ENGINEERING CERTIFICATE - I Date: / /2017 This is to certify that Mr. Raj Kumar Salvi had successfully completed the comprehensive/preliminary examination held on 25/08/2014 as required under the regulations for the Master of Technology in Mechanical Engineering (CAD/CAM). (Dr. M. A. Saloda) Associate Professor and Head Department of Mechanical Engineering C.T.A.E., Udaipur

4 MAHARANA PRATAP UNIVERSITY OF AGRICULTURE & TECHNOLGY, UDAIPUR COLLEGE OF TECHNOLOGY AND ENGINEERING CERTIFICATE II Date: / /2017 This is to certify that this thesis entitled Failure Mode and Effect Analysis for CNC machines used in GG Valves Industry submitted for the degree of Master of Technology in the subject of Mechanical Engineering, (CAD/CAM) embodies bonafide research work carried out by Mr. Raj Kumar Salvi under my guidance and supervision and that no part of this thesis has been submitted to any other degree. The assistance and help received during the course of investigation have been fully acknowledged. The draft of the thesis was also approved by the advisory committee on (Dr. M. A. Saloda) Associate Professor and Head Department of Mechanical Engineering C.T.A.E., Udaipur (Dr. Chitranjan Agarwal) Major Advisor Department of Mechanical Engineering C.T.A.E., Udaipur (Dr. S. S. Rathore) Dean C.T.A.E., Udaipur

5 MAHARANA PRATAP UNIVERSITY OF AGRICULTURE & TECHNOLGY, UDAIPUR COLLEGE OF TECHNOLOGY AND ENGINEERING CERTIFICATE- III Date: / /2017 This is to certify that this thesis entitled Failure Mode and Effect Analysis for CNC machines used in GG Valves Industry submitted by Mr. Raj Kumar Salvi to Maharana Pratap University of Agriculture & Technology, Udaipur in partial fulfillment of the requirement for the award of degree of Master of Technology in the subject of Mechanical Engineering after recommendation by the external examiner was defended by the candidate before the following members of the examination committee. The performance of the candidate in the oral examination is satisfactory. We, therefore recommend that the thesis be approved. (Dr. Chitranjan Agarwal) Major Advisor (Dr. B. P. Nandwana) Advisor (Dr. M. A. Saloda) Advisor (Dr. S. K. Jain) DRI Nominee (Dr. M. A. Saloda) Associate Professor and Head Department of ME C.T.A.E., Udaipur (Dr. S. S. Rathore) Dean C.T.A.E., Udaipur Approved Director Resident Instruction M. P.U.A.T., Udaipur

6 MAHARANA PRATAP UNIVERSITY OF AGRICULTURE & TECHNOLGY, UDAIPUR COLLEGE OF TECHNOLOGY AND ENGINEERING CERTIFICATE- IV Date: / /2017 This is to certify that Mr. Raj Kumar Salvi student of Master of Technology in the subject of Mechanical Engineering, Department of Mechanical Engineering, College of Technology and Engineering has made all corrections/ modifications in the thesis entitled Failure Mode and Effect Analysis for CNC machines used in GG Valves Industry which was suggested by the external examiner and the advisory committee in the oral examination held on. The final copies of the thesis duly bound and corrected were submitted on.. are enclosed here with for approval. (Dr. M. A. Saloda) Associate Professor and Head Department of Mechanical Engineering C.T.A.E., Udaipur (Dr. Chitranjan Agarwal) Major Advisor Department of Mechanical Engineering C.T.A.E., Udaipur

7 ACKNOWLEDGEMENT I take it to be my proud privilege to avail this opportunity to express my sincere and deep sense of gratitude to my major advisor Dr. Chitranjan Agarwal for his stimulating guidance, constructive suggestions, keen and sustained interest and incessant encouragement bestowed during the entire period of investigation, as well as critically going through the manuscript. I am gratified to record sincere thanks to the members of the advisory committee, Dr. B. P. Nandwana, Professor, Dr. M. A. Saloda, Associate Professor and Head, Department of Mechanical Engineering and Dr. S. K. Jain, Professor, Department of Processing and Food Engineering, DRI Nominee for their generous gestures and valuable suggestions end execution of the study. The author is indebted to Dr. S. Jindal, Professor, Department of Mechanical Engineering, College of Technology and Engineering, Udaipur for his ever helping nature and precious guidance that he provided me throughout my work. With profound respect, I hereby express my thanks to Dr. S. S. Rathore, Dean, College of Technology and Engineering, Udaipur for providing me the necessary facilities to carry out my work. I am highly thankful to Chief Executive Officer (CEO) of GG Valves Pvt. Ltd., Udaipur Mr. S. K. Dora for allowing me the opportunity to carry out my present work there and cooperating with me throughout. I am also thanks to my friends Ved Prakash Singh Parihar and Mayank Mehta for their moral support and encouragement. I also gratefully acknowledge all the teaching and non teaching staff members Department of Mechanical Engineering CTAE, Udaipur for their support and help I feel privileged to express my deep sense of gratitude to my parents and my beloved ones for their inspiration, affection, moral support and co-operation without which it was impossible to complete the research work. Place: Udaipur Date: (Raj Kumar Salvi) i

8 CHAPTER CONTENTS TITLE ACKNOWLEDGEMENT CONTENTS LIST OF TABLES LIST OF FIGURES SYMBOLS AND ABBREVIATIONS PAGE NO. i ii iv v vi 1 INTRODUCTION Motivation GG Valves Industry Basics of CNC Lathe Machine Common Failures in CNC Lathe Machine Techniques of Failure Data Analysis Failure Mode and Effect Analysis (FMEA) Conventional FMEA Approach Grey Relational Analysis (GRA) Objectives Thesis Overview 10 2 LITERATURE REVIEW Multi-Criteria Decision Making (MCDM) Approaches Mathematical Programming Approaches Artificial Intelligence Approaches Rule-Base System Fuzzy Rule Base System Fuzzy ART Algorithm Fuzzy Cognitive Map Integrated Approaches Other Approaches Research Gap 28 3 METHODOLOGY CNC Machine Analysis Approach Conventional FMEA Approach 31 ii

9 3.2.2 Grey Relational Analysis Approach 34 4 RESULTS AND DISCUSSIONS Data Collection Grey Relational Analysis Approach The Verification of Conventional FMEA and GRA Approach Recommended Maintenance Schedule for CNC Lathe Machines 5 CONCLUSIONS SUGGESTIONS FOR FUTURE WORK 53 SUMMARY REFERENCES ABSTRACT (ENGLISH) 64 ABSTRACT (HINDI) APPENDIX-1 67 APPENDIX-2 68 APPENDIX-3 69 APPENDIX-4 70 APPENDIX-5 71 APPENDIX-6 72 APPENDIX APPENDIX APPENDIX iii

10 TABLE LIST OF TABLES TITLE PAGE NO. 3.1 Specifications of CNC Machines Failure Data of CNC Machines Machine Downtime and Mean Time between Failure (MTBF) Conventional FMEA Results and RPN Rank The GRA Results and RPN Rank Verification of Conventional FMEA and Grey Relational Analysis 4.6 Maintenance Schedule for CNC Lathe Machines iv

11 LIST OF FIGURES FIGURE TITLE 1.1 Systematic Block Diagram of CNC Lathe Machine CNC Lathe Machine CNC Lathe Machine Parts 4 PAGE NO. 3.1 Flowchart of Conventional FMEA and Grey Relational Analysis 32 v

12 SYMBOLS AND ABBREVIATIONS S. NO. ABBREVIATION MEANING 1. AC Alternative Current 2. AHP Analytical Hierarchy Process 3. AI Aggregated Impact 4. AI Artificial Intelligence 5. APC Automatic Pallet Changer 6. CI Cost Impact 7. CNC Computer Numerical Control 8. CR Correction Ratio 9. CRT Cathode Ray Tube 10. CSTN Color Super Twisted Pneumatic 11. D Detection 12. DC Direct Current 13. DEA Data Envelopment Analysis 14. DM Degree of Match 15. ER Evidential Reasoning 16. FCM Fuzzy Cognitive Map 17. FMEA Failure Mode and Effect Analysis 18. FRPN Fuzzy Risk Priority Number 19. GRA Grey Relational Analysis 20. HOQ House of Quality 21. HOR House of Reliability 22. i Number of Failure Modes 23. I/O Input and Output 24. IFS Intuitionistic Fuzzy Set 25. ISM Interpretive Structural Model 26. k Number of Risk Factors 27. LOWA Linguistic Ordered Weighted Averaging Operator 28. MAFMA Multi-Attribute Failure Mode Analysis 29. MCDM Multi-Criteria Decision Making 30. MCS Minimum Cut Set vi

13 31. MDI Manual Data Input 32. MOI Method of Imprecision 33. MPG Manual Pulse Generator 34. MP Mathematical Programming 35. MRA Mini-max Regret Approach 36. MTBF Mean Time between Failure 37. MTTR Mean Time to Repair 38. O Occurrence 39. OWGA Ordered Weighted Geometric Averaging 40. Pd Probability of Failure not to be Detected 41. Pf Probability of Failures 42. PA Product Architecture 43. PCB Printed Circuit Board 44. PLC Programmable Logic Controller 45. QFD Quality Functional Deployment 46. RCA Risk Criticality Analyzer 47. RPC Risk Priority Code 48. RPN Risk Priority Number 49. S Severity 50. TI Time Impact 51. UPN Utility Priority Number 52. WLSM Weighted Least Square Method 53. WMoMM Weighted Mean of Maximum Method 54. Xi (k) Comparative Series Notation 55. Xo (k) Standard Series Notation 56. βk Weighting Coefficient of Risk Factor 57. ζ Grey Relation Coefficient 58. Degree of Grey Relational-ship 59. γ Grey Correlation Coefficient 60. τ Degree of Relation vii

14 INTRODUCTION CHAPTER-I This chapter presents an introduction towards the present work, the problem statement, objectives and scopes of the present work. A brief overview is also included in this chapter about the steps towards the initiative for the present research work. 1.1 MOTIVATION The CNC machine is the main equipment for advanced/modern manufacturing technology. The uses of CNC machines are increasing in modern machining processes due to inherent flexibility, machining accuracy and high productivity. However, even a single breakdown in CNC machine leads to stoppage of whole production process. Even the repair of the CNC machines is more difficult and expensive when a breakdown occurs. Thus, CNC machines are prone to create lot of troubles to the users. The reliability of CNC machines are utmost important because any breakdown may affect normal production. Therefore, there is need to improve the reliability of CNC machines. Manufacturers have to collect and analyze machine failure data and take measures to reduce downtime. Nowadays, great efforts have been given to develop advanced manufacturing technology and CNC machine worldwide to improve manufacturer ability, which is seen as an important way to accelerate economic development and improve national power and status GG Valves Industry GG Valves Pvt. Ltd., E-262, Mewar Industrial Area, Madri, Chamber Marg, Udaipur , Rajasthan, India and was established in GG Valves have been producing Bronze, SS and CS Valves (Gate, Globe, Ball Check and Cryogenic Valves) under GG Brand for nearly six decades. Over period of time, GG valves became well known for its superior quality valves. The Company is an ISO 9000 certified and with its strong quality parameters, the Company has been successful in obtaining CE marking for exports to European countries. It basically produces valves 1

15 following strict quality measures at each stage from checking raw materials, stage inspection to the final components and assembly. Each valve passes through final physical inspection including hydrostatic test before final issue. In the tradition of GG brand, GG valves industry has been continuously upgrading their products and using newer technologies, equipment and materials to meet international standards. The products ranges in the industry are given below: Steam valve Globe valve Stainless steel valves Check valve Cryogenic valve Durable cryogenic valve Drain valve Durable drain valve Gate valve With continuous improvements in the infrastructure and additions of new machines and products each year the Company has obtained a respectful position in the industry. Presently, GG valves exports valves to over 20 countries with production capacity of 150,000 valves per annum and having an employee base of 150. Groups companies of GG valves are: Western Drugs Private Limited. Ranka Organics Private Limited. Shah Khetaji Dhanji and Company. Translloy India Private Limited. 1.2 BASICS OF CNC LATHE MACHINE A CNC lathe machine is a complex system, with high-level automation and complicated structure, which employs mechanics, electronics, hydraulics and electrical mechanism. It is mainly composed of the mechanical system, Computer Numerical Control (CNC) system, hydraulic system and air feed system. The systematic diagram and their subsystems of a typical CNC lathe machine is given in figure 1.1, 1.2 and 1.3. The mechanical system includes spindle and its transmission 2

16 (fixed in a headstock), two slide axes (X, Z), carriage apron, turret or tool-holder, tailstock, bed and pedestal. The spindle, with continuous or stepped continuous speed change is driven by AC or DC spindle motor directly or through main transmission and there is a photoelectric encoder on the spindle for thread turning. X axis and Z- axis are driven by AC or DC servomotors through ball lead screws and controlled simultaneously. Figure 1.1 Systematic Block Diagram of CNC Lathe Machine Head stoke, 2. Chuck/spindle, 3. CRT, 4. Programmable Logic Controller, 5. Tail stoke 6. Guide way Figure 1.2 CNC Lathe Machine 3

17 Coolant system, 2. Turret or tool holder, 3. Turret guide way, 4. Dead centre, 5. Job holder, 6. Hydraulic system Figure 1.3 CNC Lathe Machine Parts The turret or tool-holder may exchange tools automatically. All of these are controlled by CNC system. A CNC system is the important part of modern manufacturing industry and its main functions are given below. The CNC lathe machine comprises of: Power supply unit i.e. main Printed Circuit Board (PCB), usually a microcomputer. Programmable Logic Controller (PLC) I/O PCB which connects the control panel, limit-switch, button, magnets and turret etc. Axis PCB which controls the slide axes and the spindle through semi-closed or closed loop electronic control motor drive and photoelectric encoder. Memory PCB which connects additional encoder, CRT/MDI manual data input. Manual pulse generator (MPG), backup battery and RS-232 serial communication device. The CNC system and related electronic components such as contactor switches, relays, regulators and buttons etc. are fixed in a cabinet. Other electronic 4

18 components, such as limit switches, proximity switches and encoders etc. are located on the machine. The basic and optional functions of a CNC system are linear and circular interpolation, backlash compensation, automatic co-ordinate system setting, tools offset, cutter compensation, background edit, self diagnosis and canned cycles, which is different from economical to whole functional model Common Failures in CNC Lathe Machine The failure caused by weak subsystems such as mechanical system, hydraulic system, main transmission system, electrical and electronic system and coolant system. The CNC machine failures from mechanical hand are leakage of liquid, noise over proof, overload, indexing or slider incorrect position, irregular feeding, large impact of motion parts and damaged of components. The poor quality of gasket seals causes the leakage of liquid. The main failure zone is turret and spindle of the machine. The failures caused by the hydraulic and pneumatic system are leakage, breakdown of components, overpressure, overflow, overheating, noise over proof, standard and bought-in components. The poor quality of oil pipes and connections causes the leakage and oil pipes split under high pressure. The failures caused by the transmission system are noise over proof, overload, disadjustment of flux, scratch of gear teeth or unbalance of transmission parts. The failures caused by the electrical and electronic system are motion parts in an incorrect position of Automatic Pallet Changer (APC) and motion parts that are jammed, damaged or burn contactor switches, relays, magnets, buttons and limit switches. This is the indication that there is a lack of reliability allocation and reliability screening when the CNC machine was designed and manufactured. The failure takes place in any of the given components/systems stops the production process. The identification of any of the failure requires the profound knowledge of functioning of the components. The analysis of failure at regular interval demands establishment for different techniques. 5

19 1.3 TECHNIQUES OF FAILURE DATA ANALYSIS The classification of failures is categorized into two groups: Malfunction failures and Accuracy failures. These two groups are affected by intrinsic reliability of CNC machines. Besides, the former is usually affected by operating conditions, e.g. dust, humidity, skill of operators, etc. The latter is mainly affected by operational requirements such as surface finish and tolerance. The general techniques used for failure mode analysis of CNC machines are listed as below: 1. Linear Regression Methods - Exponential Distribution Method (EDM), Weibull Distribution Method (WDM), Reyleigh Distribution Method (RDM), Guass Distribution Method (GuDM), Gamma Distribution Method (GaDM), Least Square Method (LSM), Lognormal Distribution Method (LDM) 2. Support Vector Machine (SVM) 3. Kernel Method 4. G-R Curves Analysis 5. Failure Mode and Effect Analysis (FMEA) - i. Multi-Criteria Decision Making (MCDM) - ME-MCDM, Evidence Theory, Fuzzy AHP/ANP, Fuzzy TOPSIS, D-S Theory and Prospect Theory, Grey Theory, DEMATEL, Intuitionistic Fuzzy Set, D-S Theory and D-Numbers, Ranking Technique VIKOR ii. Mathematical Programming (MP) - Linear Programming DEA/Fuzzy DEA iii. Artificial Intelligence (AI) - Rule-Base System, Fuzzy Rule-Base System, Fuzzy ART Algorithm, Fuzzy Cognitive Map iv. Integrated Approaches (IA) - Fuzzy AHP-Fuzzy Rule-Base System, WLSM-MOI-Partial Ranking Method, OWGA Operator-DEMATEL, IFS-DEMATEL, Fuzzy OWA Operator-DEMATEL, 2-Tuple-OWA- Operator, FER-Grey Theory, Fuzzy AHP-Fuzzy TOPSIS, ISM-ANP-UPN v. Other approaches Cost Based Model, Monte Carlo Simulation, Minimum Cut Set-theory (MCS), Boolean Representation Method (BRM), Digraph and Matrix approach, Kano Modal, Quality Functional Development (QFD) Probability Theory 6

20 Failure data can be reconstructed and retrieved according to specific machine, batch of machines, manufacturer, user, failure mode, failure position and so on to fit for different analytical purposes. The major tasks in reliability analyses can be performed according to the data as follows: a) Calculation of reliability characteristics, such as MTBF (Mean Time between Failures), MTTR (Mean Time to Repairs), availability, for a given machine. b) Graphical analysis techniques, such as histogram of frequency of failure, cause and effect system etc. c) Distribution patterns of failure and maintenance. 1.4 FAILURE MODE AND EFFECT ANALYSIS (FMEA) Failure Mode and Effect Analysis (FMEA) is a widely used engineering technique for identifying and prioritizing potential failure modes in systems, designs, processes and services before they occur, with the intent to eliminate them or minimize the risk associated with them. Furthermore, FMEA is an inductive approach to support risk assessment studies and the principle of FMEA is to identify potential hazards along with the focused system and to prioritize the required corrective actions or strategies. When it is used for a criticality analysis, it is also referred to as failure mode, effects and criticality analysis. Today FMEA is mainly applied in industrial production of machinery, motor cars, mechanical and electronic components. The major concern of FMEA is to emphasize the prevention of the problems linked to the proactive treatment of the system, rather than findings a solution after the failure happens. Due to its visibility and simplicity, FMEA is probably one of the most popular safety and reliability analysis tools for products and processes, which has been widely used in a number of industries as a solution to various reliability problems. Failure Mode and Effect Analysis (FMEA) is not only a method that aims to show potential failure modes, but also shows the causes and the effects of these modes. The FMEA methodology is also used in identifying controls in order to reduce the likelihood of occurrence of the failures. From the manufacturing perspective, FMEA methodology studies the equipments and the occasions in which the equipments can malfunction. It also studies the possible problems and their effects 7

21 in the manufacturing process. FMEA helps to identify potential failures in the early stages. FMEA was first developed as a formal design methodology in the 1960s by the aerospace and automotive industry with their obvious reliability and safety requirements. It can be employed to improve the safety and reliability of a system by identifying the critical potential failure modes and taking necessary preventive actions in the redesign stage of the system. This method enables to identify and analyze the effects of the failure modes as the way customers perceive them. In the FMEA application, possible failure modes, possible effects of these failure modes, prioritization of these failure modes and the corrective measures are identified with the help of a template. There are many advantages of FMEA as given below: Identify and prevent safety hazards Minimize loss of product performance or performance degradation Improve test and verification plans Improve Process Control Plans Consider changes to the product design or manufacturing process Identify significant product or process characteristics Develop Preventive Maintenance plans for in-service machinery and equipment Develop online diagnostic techniques FMEA is an engineering analysis done by a cross functional team of subject matter experts that thoroughly analyzes product designs or manufacturing processes, early in the product development process. Its objective is finding and correcting weaknesses before the product gets into the hands of the customer. FMEA can be the guide for the development of a complete set of actions that will reduce risk associated with the system, subsystem, and component or manufacturing/assembly process to an acceptable level Conventional Failure Mode and Effect Analysis (FMEA) The conventional Failure Mode and Effect Analysis (FMEA) approach is a pro-active quality tool for evaluating potential failure modes and their causes. It helps 8

22 in prioritizing the failure modes and recommends corrective measures for the avoidance of catastrophic failures and improvement of the quality of product. The conventional FMEA approach is a step-by-step process for the prioritization of different failures. The approach beings with the identification of all the ways for which failure can happen, these are called potential failure modes. The potential failure mode identifies different factors by which the prioritization is obtained. The machine downtime (time for which the machine remains stop) identifies the Severity (S). The mean time between failure determine the frequencies of the same failure mode called Occurrence (O). Then determine the Detection (D) chance of controls can detect either the cause or its failure mode after they have happened but before the customer is affected. All the obtained factors are listed for different ranking according to suitable standard nominations. Finally, Calculate the Risk Priority Number (RPN) using the factors discussed. These numbers prioritize the potential failures for the given system Grey Relational Analysis (GRA) Approach Grey Relational Analysis uses a specific concept of information. It defines situations with no information as black, and those with perfect information as white. However, neither of these idealized situations ever occurs in real world problems. In fact, situations between these extremes are described as being grey, hazy or fuzzy. Therefore, a grey system means that a system in which part of information is known and part of information is unknown. With this definition, information quantity and quality form a continuum from a total lack of information to complete information from black through grey to white. Since uncertainty always exists, one is always somewhere in the middle, somewhere between the extremes, somewhere in the grey area. Grey analysis then comes to a clear set of statements about system solutions. At one extreme, no solution can be defined for a system with no information. At the other extreme, a system with perfect information has a unique solution. In the middle, grey systems will give a variety of available solutions. Grey analysis does not attempt to find the best solution, but does provide techniques for determining a good solution, an appropriate solution for real world problems. 9

23 Grey analysis uses the factors (Severity, Occurrence and Detection) to prioritize the failure mode with a different mathematical step. Grey analysis is used because of the prioritization of failure modes with more accurate values than of conventional FMEA approach. The prioritization of the failure modes for grey theory helps in the present study needs high accuracy. 1.5 OBJECTIVES Looking to the desired need of prioritization of different failure modes in the CNC machines the following objectives are shortlisted: To accumulate the machine failure data for CNC machines in GG Valves industry. To apply conventional FMEA approach on failure data for categorization of failure modes. To apply Grey Relational Analysis for verification of conventional FMEA approach. 1.6 THESIS OVERVIEW The present work aims to achieve predicted failure modes of CNC machines using Failure Mode and Effect Analysis techniques. The present thesis work is organized in the following manner as discussed below. Chapter 1 briefly introduces the problems associated with CNC failures, characteristics of FMEA and summaries the specific objectives of the project. Chapter 2 provides a thorough review of the literature of current research activities related to FMEA. This includes the knowledge of multi-criteria decision making, mathematical programming, artificial intelligence, integrated approaches and also the improvement of product quality. The deficiencies and limitations of theories/methods are also discussed and the limitations in the literature were then used to deduct a research gap. Chapter 3 describes both conventional FMEA and Grey Relational Analysis approaches that have been used to achieve the objectives raised throughout the project. 10

24 Chapter 4 gives the discussions of the results obtained for the calculated values of RPN from FMEA approach. The results of conventional FMEA are further verified using GRA approach. Chapter 5 summarizes the contributions of this study for the understanding of the prioritization of failure modes and improvement of production rate for the industry. At last, suggestions for future work, summary, references and abstract has been presented. 11

25 CHAPTER-II LITERATURE REVIEW The literature survey is the brief review of the accredited research on CNC machines failure mode. The purpose of the literature review is conveying about the ideas and knowledge established on the present topic with a view of strengths and weakness. In this section, the literature search on risk evaluation in FMEA for priority ranking of failure modes. The source used for this study was academic journal articles published between 1992 and The present chapter includes articles that report on a method or technique that specifically aims at overcoming some of the drawbacks of the conventional FMEA. Vast majority of risk priority models are found in the literature to improve the criticality analysis process of FMEA. Therefore the study proposes a framework for classifying the reviewed papers depending upon the failure mode prioritization methods that have been identified. In this review, the study divides the methods used in the literature into five main categories, which are Multi-Criteria Decision Making (MCDM), Mathematical Programming (MP), Artificial Intelligence (AI), Hybrid Approaches and others. 2.1 MULTI-CRITERIA DECISION MAKING (MCDM) APPROACHES Franceschini and Galetto (2001) presented a Multi-Expert MCDM (ME- MCDM) technique for carrying out the calculation of the risk priority of failures in FMEA, which is able to deal with the information provided by the design team, normally given on qualitative scales, without necessitating an arbitrary and artificial numerical conversion. The method considered each decision-making criterion as a fuzzy subset over the set of alternatives to be selected. After the aggregation of evaluations expressed on each criterion for a given alternative, the failure mode were determined with the maximum Risk Priority Code (RPC). If two or more failure modes have the same RPC a more detailed selection was provided to discriminate their relative ranking. Wu et al. (2016) used the Computer numerical control (CNC) machines for performing a Multi- Criteria Group Decision Making (MCGDM) technique based on the fuzzy VIKOR method. Linguistic variables represented by triangular fuzzy 12

26 numbers are used to reflect the preferences of decision maker for the criteria importance weights and the performance ratings. After the individual preferences are aggregated or after the separation values are computed, they are then defuzzified. Yang et al. (2010) described a new fuzzy FMEA model integrated with fuzzy linguistic scale method for the analysis of a type of CNC lathe. The model proposed a risk-space diagram to explicit the relationship of S, O and D where the risk priority number is calculated by weighted Euclidean Distance formula and centroid defuzzification based on Alpha-level. The results indicated that the fuzzy FMEA used in CNC lathe is a reasonable method corresponding to the manufacturing with a supporting plan for manufacturing. Wang et al. (2016) proposed the Failure Mode, Effects and Criticality Analysis (FMECA) which does not take opinions of different team members when considering the assignment of criticality. In this paper, improved criticalities (ICR) were calculated and failures of feed systems were prioritized by IFMECA based on the failure data of the feed system. It is observed in the results that vibration or oscillation, motion parts output failure, and inaccurate re-home have more negative impacts on the feed system. Li et al. (2016) proposed a study on the expert judgment of failure mode analysis method where the possible distribution of the attribute of the judgment were established and the information fusion of the possibility distribution were implemented using the comprehensive analysis method. The values are assigned to the weights which combines the variation coefficient method and the subjective weighting method. The possibility values of the RPN are calculated on a machining centre and then ranked. Gupta G. and Mishra R.P. (2016) did study on the maintenance strategies for each failure mode of functionally significant item of conventional milling machine. This paper presents a study on reliability-centered maintenance with fuzzy logic and its comparison with conventional method where failure mode and effect analysis were introduced integrating with fuzzy linguistic scale method. The results for risk priority number were based on weighted Euclidean distance formula and centroid defuzzification where the criticality ranking was decided, and appropriate maintenance strategies were suggested for each failure mode. 13

27 Failure Mode and Effects Analysis (FMEA) has been extensively used for examining potential failures in many industries. Unlike the traditional FMEA, the risk factors of Occurrence (O), Severity (S) and Detection (D) of each failure mode are evaluated by linguistic terms and fuzzy ratings in this research, and their relative weights are considered, which make the results more useful and practical. Both grey theory and fuzzy theory are applied in FMEA, in which fuzzy set theory is used to calculate the Fuzzy Risk Priority Numbers (FRPNs), and grey theory is applied to calculate the grey relational coefficient. The rankings of the failure modes are both determined by FRPNs and the grey theory by Zhou and Thai (2016). Ambekar et al. (2013) worked on Failure Mode and Effects Analysis (FMEA) which is a procedure of product development and operations management for analysis of potential failure modes within a system for classification by the severity and likelihood of the failures. A successful FMEA activity helps a team to identify potential failure modes based on past experience with similar products or processes, enabling the team to design those failures out of the system with the minimum of effort and resource expenditure, thereby reducing development time and costs. The purpose of FMEA for prioritizing failure modes is presented by Sofyalioglu and Ozturk (2012) for three different methods. These methods are traditional approach, Grey Relational Analysis (GRA) and Fuzzy Analytic Hierarchy Process (FAHP) to estimate weights for the risk factors. According to the findings, integration of GRA and fuzzy AHP revealed a difference in prioritizing failure modes from the methods with the assumption of equal weights. An FMEA using the group-based Evidential Reasoning (ER) approach to capture FMEA team members diversity opinions and prioritize failure modes under different types of uncertainties such as incomplete assessment, ignorance and intervals was presented by Chin et al. (2009b). The risk priority model was developed using the group-based ER approach, which includes assessing risk factors using belief structures, synthesizing individual belief structures into group belief structures, aggregating the group belief structures into overall belief structures, converting the overall belief structures into expected risk scores, and ranking the expected risk scores using the Mini-max Regret Approach (MRA). 14

28 The study of breakdown maintenance for the machinery failure was performed by Degu and Moorthy (2014). The failure modes and their causes were identified for each machine, the three key indices (Severity, Occurrence and Detection) reassessed and the analysis was carried out with the help of Machine Failure Mode and Effect Analysis (MFMEA) Worksheet. The research work results in a considerable machine downtime and disrupting the continuous production of pipes. Yang et al. (2011) also adopted evidence theory to aggregate the risk evaluation information of multiple experts. However, all individual and interval assessment grades were assumed to be crisp and independent of each other in the proposed model. It did not considerate the occasion in FMEA where an assessment grade may represent a vague concept or standard and there may be no clear cut between the meanings of two adjacent grades. Mhetre and Dhake (2012) studied to identify and eliminate current and potential problems from a bending process of a company. Ishikawa Diagram and the Failure mode Effect Analysis is aimed to reduce errors and shorten the development duration and increase product reliability. It prioritizes potential failures according to their risk and drives actions to eliminate or reduce their likelihood of occurrence. They worked on the goal of quality and reliability systems to achieve customer satisfaction. A system cannot be reliable if it does not have high quality. Likewise, a system cannot be of high quality if it is not reliable. FMEA provides an easy tool to determine which risk has the greatest concern and therefore an action to prevent a problem before it arises. The development of these specifications will ensure the product will meet the defined requirements studied by Waghmare et al. (2014). Braglia (2000) developed a Multi-Attribute Failure Mode Analysis (MAFMA) approach based on the Analytic Hierarchy Process (AHP) technique, which views the risk factors (O, S, D and expected cost) as decision criteria, possible causes of failure as decision alternatives and the selection of cause of failure as decision goal. The pair wise comparison matrix was used to estimate criterion weights and the local priorities of the causes in terms of the expected cost attribute. The conventional scores for O, S and D were normalized as the local priorities of the causes with respect to O, S and D 15

29 respectively, and the weight composition technique in the AHP was utilized to synthesize the local priorities into the global priority, based on which the possible causes of failure were ranked. Making reference to Braglia (2000), Carmignani (2009) presented a priority-cost FMECA (PC-FMECA), which allows for the calculation of a new RPN and the introduction of the concept of profitability taking into consideration the corrective action cost. On the other hand, Hu et al. (2009) presented a green component risk priority number (GC-RPN) to analyze the risks of green components to hazardous substance. Fuzzy AHP was applied to determine the relative weightings of risk factors. Then the GC-RPN was calculated for each one of the components to identify and manage the risks derived from them. An advanced version of the FMECA, called Analytic Network Process (ANP)/RPN, which enhances the capabilities of the standard FMECA taking into account possible interactions among the principal causes of failure in the criticality assessment worked by Zammori and Gabbrielli (2011). According to the ANP/RPN model, O, S and D were split into sub-criteria and arranged in a hybrid (hierarchy/network) decision structure that, at the lowest level, contains the causes of failure. Starting from this decision-structure, the RPN was computed by making pairwise comparisons. In order to clarify and to make evident the rational of the final results a graphical tool was also presented in the paper. An alternative multi-attribute decision-making approach called fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach for FMECA, which considers the failure causes as the alternatives to be ranked, the risk factors O, S and D related to a failure mode as criteria. The failures were prioritized based on the measurement of the Euclidean distance of an alternative from an ideal goal. In the proposed fuzzy TOPSIS approach, the three risk factors and their corresponding weights of importance were allowed to be assessed using triangular fuzzy numbers rather than precise crisp numbers, giving a final ranking for failure causes that are easy to interpret presented by Braglia et al. (2003b). Chang et al. (1999) used Fuzzy method and Grey theory for FMEA, where fuzzy linguistic variables were used to evaluate the risk factors O, S and D, and Grey Relational Analysis was applied to determine the risk priority of potential causes. To carry out the Grey Relational Analysis, fuzzy linguistic variables were defuzzified as 16

30 crisp values, the lowest levels of the three risk factors were de-fined as a standard series, and the assessment information of the three risk factors for each potential cause was viewed as a comparative series, whose grey relational coefficient and degree of relational with the standard series were computed in terms of the grey theory. Stronger degree of relational means smaller effect of potential cause. In Chang et al. (2001), they also utilized the grey theory for FMEA, but the degrees of relational were computed using the traditional scores 1 10 for the three risk factors rather than fuzzy linguistic variables. Similar applications of fuzzy method and grey theory for prioritization of failure modes in FMEA can also be found in Sharma, Kumar, and Kumar (2008b, 2007d), Pillay and Wang (2003) and Sharma and Sharma. Geum et al. (2011) proposed a systematic approach for identifying and evaluating potential failures using a service specific FMEA and Grey Relational Analysis. Firstly, the service-specific FMEA was provided to reflect the servicespecific characteristics, incorporating 3 dimensions and 19 sub-dimensions to represent the service characteristics. As the second step, under this frame-work of service-specific FMEA, the risk priority of each failure mode was calculated using Grey Relational Analysis. In this paper, Grey Relational Analysis was applied with a two-phase structure: one for calculating the risk score of each dimension: O, S and D, and the other for calculating the final risk priority. The VIKOR method, which was developed for multi-criteria optimization for complex systems, to find the compromise priority ranking of failure modes according to the risk factors in FMEA applied by Liu et al. (2012). In the methodology, linguistic variables, expressed in trapezoidal or triangular fuzzy numbers, were used to assess the ratings and weights for the risk factors O, S and D. The extended VIKOR method was used to determine risk priorities of the failure modes that have been identified. 2.2 MATHEMATICAL PROGRAMMING APPROACHES Wang et al. (2009b) proposed Fuzzy Risk Priority Numbers (FRPNs) for prioritization of failure modes to deal with the problem that it is not realistic in real applications to determine the risk priorities of failure modes using the RPNs because they require the risk factors of each failure mode to be precisely evaluated. In the paper, the FRPNs were defined as fuzzy weighted geometric means of the fuzzy 17

31 ratings for O, S and D, and can be computed using a level sets and linear programming models. Finally, the FRPNs were defuzzified using Centroid Defuzzification Method for ranking purpose. In addition, Gargama and Chaturvedi (2011) employed a benchmark adjustment search algorithm, rather than the linear programming approach, to determine the weighted fuzzy geometrical means of a level sets to compute the FRPNs. In Chen and Ko (2009a and 2009b), the FRPNs was defined as Fuzzy Ordered Weighted Geometric Averaging (FOWGA) (Xu and Da, 2003) of the three risk factors. Garcia et al. (2005) presented a fuzzy Data Envelopment Analysis (DEA) approach for FMEA in which typical risk factors O, S and D were modeled as fuzzy sets, and the fuzzy possibility DEA model introduced by Lertworasirikul et al. (2003) was used for determining the ranking indices among failure modes. Chang and Sun (2009) also applied DEA to enhance the assessment capability of FMEA; however, the inputs (O, S and D) of FMEA were crisp values (from 1 to 10) instead of fuzzy sets in their proposed model. Chin et al. (2009a) argued that Garcia et al. (2005) approach is computationally very complicated and also could not produce a full ranking for the failure modes to be prioritized. Based on these arguments, they proposed a DEA based FMEA which takes into account the relative importance weights of risk factors, but has no need to specify them subjectively. The two risks were then geo-metrically averaged to reflect the overall risks of the failure modes, based on which the failure modes can be prioritized. Incomplete and imprecise information on the evaluation of risk factors was also considered in the FMEA. 2.3 ARTIFICIAL INTELLIGENCE APPROACHES Rule-Base System A modified approach for prioritization of failures in a system FMEA, which uses the ranks , called Risk Priority Ranks (RPRs), to represent the increasing risk of the 1000 possible Severity, Occurrence and Detection combinations. These 1000 possible combinations were tabulated by an expert in order of increasing risk and can be represented in the form of if-then rules. The failures having a higher rank were given a higher priority than those having a lower rank presented by Sankar and Prabhu (2001). 18

32 2.3.2 Fuzzy Rule-Base System Bowles and Pelaez (1995) described a fuzzy logic based approach for prioritizing failures in a system FMECA, which uses linguistic variables to describe O, S, D and the riskiness of failure. The relationships between the riskiness and O, S, D were characterized by a fuzzy if-then rule base which was developed from expert knowledge and expertise. Crisp ratings for O, S and D were fuzzified to match the premise of each possible if-then rule. All the rules that have any truth in their premises were fired to contribute to the fuzzy conclusion set. The fuzzy conclusion was then defuzzified by the Weighted Mean of Maximum Method (WMoMM) as the ranking value of the risk priority. Moss and Woodhouse (1999) also suggested a similar fuzzy logic approach for criticality analysis. Based on the fuzzy logic approaches described above, Xu et al., (2002) developed a fuzzy FMEA assessment expert system for diesel engine s gas turbocharger, Zafiropoulos and Dialynas (2005) presented a fuzzy FMECA assessment system for a power electronic devices such as a Switched Mode Power Supply (SMPS), Chin et al. (2008) developed a fuzzy FMEA based product design system called EPDS-1, and Nepal et al. (2008) presented a general FMEA frame-work for capturing the failures due to system/component interactions at the Product Architecture (PA) level. A criticality assessment approach based on qualitative rules which ranks the risks of potential causes of failure presented by Puente et al. (2002). The methodology assigned a risk priority class to each cause of failure in an FMEA, depending on the importance given to the three risk factors (O, S and D) related to a failure mode. The structure of the qualitative rules was of the if-then rule type and all the 125 rules in the FMEA were shown in the form of a three-dimensional graph. In order to optimize the risk-discrimination capabilities of the different causes of failure, a modified version of the technique integrating with fuzzy logic was also proposed by the authors. Pillay and Wang (2003) proposed a fuzzy rule base approach that does not require a utility function to define the O, S and D considered for the analysis. This was achieved by using information gathered from experts and integrating them in a formal way to reflect a subjective method of ranking risk. The proposed approach needs to set up the membership functions of the three risk factors O, S and D first. 19

33 Each of the failure modes was then assigned a linguistic variable representing the three risk factors. Using the fuzzy rule base generated, these three variables were integrated to pro-duce linguistic variables representing the risk ranking of all the failure modes. Fuzzy Rule-Based Bayesian Reasoning (FuRBaR) approach for prioritizing failures in FMEA. The technique was specifically developed to deal with some of the drawbacks concerning the use of conventional fuzzy logic (i.e. rule-based) methods in FMEA. In their approach, subjective belief degrees were assigned to the consequent part of the rules to model the incompleteness encountered in establishing the knowledge base. A Bayesian Reasoning mechanism was then used to aggregate all relevant rules for assessing and prioritizing potential failure modes proposed by Yang et al. (2008). A fuzzy FMEA model for prioritizing failures modes based on the degree of match and fuzzy rule-base to overcome some limitations of traditional FMEA. The proposed model employed the belief structure for the assessment of risk factors, and then converted randomness in the assessed information into a convex normalized fuzzy number proposed by Gargama and Chaturvedi (2011). The Degree of Match (DM) was used thereafter to estimate the matching between the assessed information and the fuzzy sets of risk factors. This computed DM then became the inputs to the fuzzy rule-based systems where rules were processed resulting in failure classification with degree of certainty Fuzzy ART Algorithm Keskin and Ozkan (2009) applied the Fuzzy Adaptive Resonance Theory (Fuzzy ART) neural networks to evaluate RPN in FMEA. In the study, Occurrence, Severity and Detection values constituting RPN value were evaluated separately for each input. RPN values composed inputs and each input in its own was presented as O, S and D to the system. In each case, an input composed of three data (O, S and D) was presented to the system by efficient parameter results obtained from application of FMEA on test problems and similar inputs were clustered according to the three parameters. Finally, arithmetic mean of the input values in each obtained failure class was used for prioritization. 20

34 2.3.4 Fuzzy Cognitive Map Pelaez and Bowles (1996) applied Fuzzy Cognitive Maps (FCMs) to model the behavior of a system for FMEA. The FCM was a diagram to represent the causality of failures with failure node and causal relation path. The path was described by using linguistic variables such as some, always, often and relative scales were assigned for each term. Then min-max inference approach was used to evaluate the net causal effect on any given node and weighted mean of maximum method was used as defuzzification technique to extract the resulting confidence values on linguistic variables. 2.4 INTEGRATED APPROACHES A fuzzy-rpns-based method for FMEA under uncertainty integrating Weighted Least Square Method (WLSM), the Method of Imprecision (MOI) and partial ranking method. In this study, multi-granularity linguistic term sets were adopted by decision makers in FMEA team for expressing their judgments; a fuzzy WLSM was cited for aggregating these judgments in order to form a consensus group judgment; the MOI incorporated with a nonlinear programming model was used for calculating the fuzzy RPNs based on the group judgment; the partial order method based on fuzzy preference relations was employed for the final ranking of failure modes according to their scores of fuzzy RPNs described by Zhang and Chu (2011). The application of FMEA to risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. In the study, Severity (S) was referred to as Impact (I) and had three dimensions: Cost Impact (CI), Time Impact (TI) and Scope/quality Impact (SI) extended by Abdelgawad and Fayek (2010). Fuzzy AHP was con-ducted to aggregate CI, TI and SI into a single variable entitled Aggregated Impact (AI). Based on the assigned values for O and D together with the calculated AI, fuzzy FMEA expert system sup-ported by fuzzy if-then rules was used to analyze and prioritize different risk events. Besides, a software system entitled Risk Criticality Analyzer (RCA) was developed to implement the pro-posed framework. Liu et al. (2011) proposed a risk priority model for FMEA using Fuzzy Evidential Reasoning (FER) approach and Grey Theory. The FER approach was used to model the diversity and uncertainty of FMEA team members assessment information, and the Grey Relational Analysis was utilized to determine the risk 21